Method, system, and computer program product for determining a patient radiation and diagnostic study score

ABSTRACT

A method for determining a patient radiation and diagnostic study score associated with past diagnostic radiologic tests. In light of the obvious benefits of diagnostic radiology, the risks inherent in its use are often overlooked. Ionizing radiation, which is a component of much, but not all, diagnostic radiology, carries with it a small risk of inducing cancer every time it is used. This additional risk, known as “Lifetime Attributable Risk,” is layered on top of an individual&#39;s lifetime base risk of invasive cancer. The present method for determining a patient radiation and diagnostic study score provides right time, right place, and right format radiology information to assist providers in their medical decision-making. With greater awareness of recent study history, and individually contextualized risk and benefit considerations, providers are more likely to decrease their overall usage of diagnostic radiology and better counsel their patients on future risk.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplication Ser. No. 61/952,353, filed on Mar. 13, 2014, all of which isincorporated by reference as if completely written herein.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was not made as part of a federally sponsored research ordevelopment project.

TECHNICAL FIELD

The present invention relates to the assessment of risk secondary toionizing radiation exposure as it relates to its use in the medicalfield.

BACKGROUND OF THE INVENTION

The use of diagnostic radiology has been dramatically increasing formany years with proportional financial and patient safety effects.Between 2000 and 2007 the use of imaging studies grew faster than thatof any other physician service in the Medicare population. Currently,there are more than 70 million CT scans performed in the United Statesevery year. A study by the influential group America's Health InsurancePlans claims that 20% to 50% of all “high-tech” imaging provides nouseful information and may be unnecessary, and the radiation exposurefrom these scans may lead to thousands of cancer-related deaths. Reportslike these have led to cost and safety concerns among key federalagencies like the Congressional Budget Office, Government AccountabilityOffice, and Medicare Payment Advisory Commission, and steps have beentaken in recent years to reduce reimbursements for imaging as one meansof reducing overall usage and exposure.

The overuse of diagnostic radiology is partially explained by the factthat it has been enormously helpful in the areas of non-invasivediagnosis. Some conditions that previously required general surgery todiagnose can now be painlessly and quickly found with a CT Scan, withmuch less inherent risk to the patient. The diagnostic radiology fieldis also responsible for saving many patients' lives by facilitating thetimely diagnosis of dangerous medical conditions, such as internalbleeding. As a result of these obvious benefits of diagnostic radiology,the risks inherent in its use are often overlooked. Ionizing radiation,which is a component of much (but not all) diagnostic radiology, carrieswith it a small risk of inducing cancer every time it is used. Thisadditional risk is known as “Lifetime Attributable Risk” or “LAR”. TheLAR due to medical imaging is layered on top of an individual's lifetimebase risk of invasive cancer, which is approximately 37% for women and45% for men.

The LAR due to diagnostic radiology has historically been assumed to berelatively low, and usually less than 1%, but a sub-group of individualsin society exists with a much higher LAR. This sub-group representsapproximately 2% of the population as found in the large studypopulation used to develop the Rads Scoring system. Although this groupof individuals is numerically small, they account for 25% of all the CTScans in the study group. This finding conforms with the generalunderstanding that the top 1% of medical users consume about 20% of theresources and the top 5% consume nearly 50%. In the case of radiologyoveruse, the top 2% of patient consumers are also absorbing much of theadditional cancer risk for the population. In many cases, thisadditional cancer risk approaches 10% and in rare cases, the additionalrisk approaches 20%.

The above begs the question, if radiology overuse is an accepted problemthat results in financial waste and patient risk with often times littlebenefit, why does it continue? There are several possible answers.Firstly, medico-legal concerns amongst providers are generally acceptedto be a source of defensive medicine practice patterns with resultantoveruse of diagnostics of all types, including radiology. Secondly,patient satisfaction leads many institutions down the path of givingpatients what they want, be it x-rays, antibiotics, or pain medications.The Federal Government's incorporation of patient satisfaction intoreimbursement equations creates direct financial incentives to makepatients happy, even when it may not be in their overall best interest.Thirdly, hidden or difficult-to-get-to information leads to repeatdiagnostics when it becomes easier to order it again rather than findand access previous results. Lastly, there are many times a providerwill order a test without knowledge of recent testing that may haveotherwise dissuaded them from ordering additional tests.

There are of course many more possible reasons behind the overuse ofdiagnostic radiology, but a common thread emerges in the above. Ourcurrent healthcare system provides many more reasons to order aradiologic test as compared with reasons to not order a radiologic test.At least one component of the solution to the problem of radiologyoveruse must therefore involve the creation of a reason (or reasons) notto order radiological tests. Some of these reasons could be (a)increasing awareness of previous testing, (b) increasing awareness ofadditional radiation risk exposure for the patient, (c) institutingchecks and balances when ordering additional studies for high riskpatients, (d) exposure to financial risk for inappropriate ordering, and(e) medico-legal exposure for unnecessary testing that impacts patientsafety.

One additional problem with radiology overuse is that even when it issuspected, many providers lack the ability to properly contextualize theamount of overuse and apply it to a risk/benefit analysis. Therefore,clinical decisions are often made without a true understanding ofaccumulated risk. In a similar vein, discussions with patients aboutradiology overuse are often lacking in content and relevancy, or worse,contaminated with misinformation. Therefore, an additional component ofany solution must be the creation of relevant and contextual informationfor a provider and patient to consider. In order for this information tobe relevant and contextual, it must relate to identifiable, “down toearth”, quantities and concepts.

The present method, system, and computer program product for determininga patient Radiation and Diagnostic Study Score provides right time,right place, and right format radiology information to providers toassist them in their medical decision-making. With greater awareness ofrecent study history, and individually contextualized risk and benefitconsiderations, providers are more likely to decrease their overallusage of diagnostic radiology, and also be enabled to better counseltheir patients on future risk.

SUMMARY OF THE INVENTION

A method for determining a patient radiation and diagnostic study scoreassociated with past diagnostic radiologic tests. In light of theobvious benefits of diagnostic radiology, the risks inherent in its useare often overlooked. Ionizing radiation, which is a component of much,but not all, diagnostic radiology, carries with it a small risk ofinducing cancer every time it is used. This additional risk, known as“Lifetime Attributable Risk,” is layered on top of an individual'slifetime base risk of invasive cancer. The present method fordetermining a patient radiation and diagnostic study score providesright time, right place, and right format radiology information toassist providers in their medical decision-making. With greaterawareness of recent study history, and individually contextualized riskand benefit considerations, providers are more likely to decrease theiroverall usage of diagnostic radiology and better counsel their patientson future risk.

BRIEF DESCRIPTION OF THE DRAWINGS

Without limiting the scope of the present method, system, and program,referring now to the drawings and figures:

FIG. 1 shows an illustrative table showing a hypothetical databaserecord of a patient, in accordance with an embodiment of the invention;

FIG. 2 shows an illustrative table showing a hypothetical correlationbetween radiologic tests and a measure of ionizing radiation, inaccordance with an embodiment of the invention;

FIG. 3 shows an illustrative table showing a hypothetical correlationbetween age and an age adjustment factor, in accordance with anembodiment of the invention;

FIG. 4 shows an illustrative table showing a hypothetical patient recordwith a measure of ionizing radiation in mSv, an Age Adjustment Factor, aTest Specific Lifetime Attributable Risk, and a Total LifetimeAttributable Risk, in accordance with an embodiment of the invention;

FIG. 5 shows an illustrative table showing a hypothetical population'sTotal Lifetime Attributable Risk, in accordance with an embodiment ofthe invention;

FIG. 6 shows an illustrative table showing a hypothetical patient'sScaled Lifetime Attributable Risk, in accordance with an embodiment ofthe invention;

FIG. 7 shows an illustrative table showing a hypothetical patient'sRadiation and Diagnostic Study Score with Recent Study Indicator, inaccordance with an embodiment of the invention;

FIG. 8 shows an illustrative table showing a hypothetical Previous StudyFactor, in accordance with an embodiment of the invention;

FIG. 9 shows an illustrative table showing a hypothetical Time DecayElement, in accordance with an embodiment of the invention;

FIG. 10 shows an illustrative table showing a hypothetical Usage MetricCalculation, in accordance with an embodiment of the invention;

FIG. 11 shows an illustrative diagram of an embodiment of the invention;

FIG. 12 shows an illustrative diagram of an embodiment of the invention;

FIG. 13 shows an illustrative diagram of an embodiment of the invention;

FIG. 14 shows an illustrative diagram of an embodiment of the invention;

FIG. 15 shows an illustrative table showing a hypothetical record of apatient's radiologic tests, in accordance with an embodiment of theinvention;

FIG. 16 shows an illustrative table showing a hypothetical correlationbetween radiologic tests and a measure of ionizing radiation, inaccordance with an embodiment of the invention;

FIG. 17 shows an illustrative graph, not to scale, of General PopulationTotal Lifetime Attributable Risk vs. Scaled Value for Risk, inaccordance with an embodiment of the invention;

FIG. 18 shows an illustrative graph, not to scale, of General PopulationTotal Lifetime Attributable Risk vs. Adjusted Scaled Value for Risk, inaccordance with an embodiment of the invention;

FIG. 19 shows a portion of a hypothetical patient report, in accordancewith an embodiment of the invention;

FIG. 20 shows a portion of a hypothetical patient report, in accordancewith an embodiment of the invention;

FIG. 21 shows a portion of a hypothetical patient report, in accordancewith an embodiment of the invention;

FIG. 22 shows an illustrative table showing a hypothetical resolutionmetric calculation, in accordance with an embodiment of the invention;

FIG. 23 shows an illustrative diagram of an embodiment of the invention;and

FIG. 24 shows an illustrative diagram of an embodiment of the invention.

These figures are provided to assist in the understanding of exemplaryembodiments as described in more detail below and should not beconstrued as unduly limiting. In particular, the relative spacing,positioning, sizing and dimensions of the various elements illustratedin the drawings are not drawn to scale and may have been exaggerated,reduced or otherwise modified for the purpose of improved clarity. Thoseof ordinary skill in the art will also appreciate that a range ofalternative configurations have been omitted simply to improve theclarity and reduce the number of drawings.

DETAILED DESCRIPTION OF THE INVENTION

The claimed method, system, and computer program product for determininga patient radiation and diagnostic study score (10) and report enables asignificant advance in the state of the art.

At the heart of the invention is a radiologic scoring system that raisesawareness to the availability of patient specific radiologic informationrelevant to lifetime attributable risk (400), also referred to as LAR,and diagnostic study usage. This scoring system, which generates valuesknown as radiation and diagnostic study score (10), also referred to asa “rads score”, which in some embodiments ranges from 000 to 999. Higherscores translate to greater lifetime attributable risk (LAR). A keyelement of the scoring system is that even though radiation exposure istheoretically limitless, the radiation and diagnostic study score (10)is range bound. Furthermore, the radiation and diagnostic study score(10) is “naturally relative” as it is based on an assessment of a largestudy population of individuals who have used diagnostic radiology inthe past. The use of a large study population allows for the creation ofan intuitive scoring system based on the rank ordered percentilecontribution of an individual's risk to the sum total of thepopulation's risk.

To further explain the nature of the radiation and diagnostic studyscore (10) system, let us evaluate a universe (closed system) of 25individuals (Persons A-Y), all of variable age, who have been subject toonly 5 possible radiologic tests (200) (Test 1-5). Furthermore, let usassume that a history of the radiologic tests (200) performed on eachpatient has been stored in a database (100) accessible to a scoreprocessor.

FIG. 1 represents radiologic test (200) data associated with Patient Afound in the database (100) for a scoring period, which in this exampleis the life of Patient A. This record contains 20 studies all performedat varying times. Each of the patients in the universe has their ownrecord containing similar data. The scoring period is not required to bethe life of a patient, but it is preferable when such radiologic test(200) data is available, although such data may not be readilyaccessible in electronic form for some patients. Generally the scoringperiod is at least 1 year, preferably at least 5 years, more preferablyat least 10 years, even more preferably at least 15 years, and ideallythe life of the patient.

Radiology studies that use ionizing radiation increase lifetimeattributable risk (400), but not all radiology studies use ionizingradiation. Furthermore, those studies that use ionizing radiation usevarying amounts of it. For the purposes of developing a radiation anddiagnostic study score (10), the present method assigns a value to eachradiologic test (200) in the scoring period that represents the amountof radiation used, if such a measure of ionizing radiation is notreadily available in the database (100). The measure of ionizingradiation may be represented in numerous ways and may utilize severalchoices of units. In one embodiment the measure of ionizing radiation isthe Sievert (Sv), which represents the stochastic (or cancer inducing)risk of each study to the whole body. In use, Sieverts are usuallyrepresented as milli-Sieverts (mSv) and are dependent on the amount ofradiation used and where on the body it is focused. The actual mSv foreach radiologic test (200) is dependent on machine settings, imagingprotocol, and patient size characteristics. In some cases this actualvalue can be stored in the database (100). In cases where no mSv isstored, an average value can be assigned based on the type of radiologictest (200). FIG. 2 represents a list of the average mSv values for eachof the generic radiologic tests (200), Test 1-5 of this example.

As mentioned above, the cancer inducing risk of ionizing radiation isdependent on the amount and location of the radiation exposure. It isalso dependent on the age of the patient as younger patients are bothmore sensitive to radiation and also have a longer life expectancy overwhich there is more time for cancer to develop. Studies of the atomicbomb events of World War II have resulted in generally accepted ageadjustments for a given amount of radiation exposure. These adjustmentshave been published, or can be extrapolated from, the Biologic Effectsof Ionizing Radiation (BEIR) series of reports. One embodiment of theinvention includes an age adjustment factor (300) to scale the measureof ionizing radiation for each radiologic test (200). For the purposesof this example, a sex averaged age adjustment factor (300), alsoreferred to as AAF, is determined by the score processor based on thefollowing formula: age adjustment factor (300)=(2−(age inyears/12.5)+1), minimum value=1. This formula effectively yields a valueof 3 at age 0 that linearly decreases to 1 at age 25 and then goes nolower. FIG. 3 represents the one exemplary individual age adjustmentfactor (300) values for ages 1 to 25+. One skilled in the art willappreciate that the score processor may utilize any number of linear ornonlinear declining line or curve methodologies to determine the ageadjustment factor (300) starting with an initial value at a young age,preferably birth, and declining to a terminal value at a predeterminedage, which is 25 in the present embodiment. The young age is preferablyless than 5 years old, more preferably less than 2.5 years, even morepreferably less than 1 year, and ideally birth, while the predeterminedage of the terminal value is preferably at least 15 years, morepreferably at least 20 years, and most preferably at least 25 years. Theage adjustment factor (300) is not limited to the example of this oneembodiment.

As mentioned above, lifetime attributable risk (400) in the setting ofcalculating a radiation and diagnostic study score (10) is the extrarisk, in this embodiment measured as a percent, of getting cancer fromthe radiation received during diagnostic imaging occurring during thescoring period. The lifetime attributable risk (400) contribution foreach diagnostic study is obtained by the score processor, whichcalculates the product of the mSv and the lifetime attributable risk(400) for each radiologic test (200) and divides by 100. The patient'stotal lifetime attributable risk (400) is the sum of the lifetimeattributable risk (400) for each radiologic test (200) during thescoring period. FIG. 4 represents the addition of mSv, computed ageadjustment factor (300), computed study lifetime attributable risk(400), and computed total lifetime attributable risk (400). In thisexample, Patient A has acquired an extra 0.77% risk of cancer as aresult of their exposure to ionizing radiation during radiologic tests(200).

Knowing that Patient A has acquired an additional 0.77% risk of cancerwill help inform a provider as to the current additional risk of cancerfor the patient. Certainly, the patient has benefitted from at leastsome of the radiologic tests (200) that were performed in the past soalthough a provider knows the absolute lifetime attributable risk (400)value, they may have a hard time understanding whether the benefitsoutweigh the risks. The provider also doesn't know whether the lifetimeattributable risk (400) value is below normal, average, or well abovenormal. If the provider could somehow understand how this patient'slifetime attributable risk (400) measures up to all other patientlifetime attributable risk (400) in the population, they may betterunderstand the context (relative value) and also be able to betterpredict future risks and benefits.

The database (100) containing Patient A's data may also contain data forother patients in the population, or such general population data mayreside in a separate database or quick reference chart, diagram, orequation. Regardless of the form accessed by the score processor, atsome point the same methods applied to Patient A are used by a scoreprocessor to determine the total lifetime attributable risk (400) for alarge quantity of patients that accurately reflect the generalpopulation, which in this closed system example of 25 patents includesevery patient. Thus, FIG. 5 represents an ordered list of total lifetimeattributable risk (400) values for all patients in this exemplarypopulation. Once the total lifetime attributable risk (400) value isknown for every patient, the total lifetime attributable risk (400)values can be transformed, via the score processor, to a scaled lifetimeattributable risk (410) value representing the rank ordered, percentcontribution to the total population lifetime attributable risk (400).This process is demonstrated in FIG. 6. In FIG. 6, one can see that thesum of all patient total lifetime attributable risk (400) values, alsothought of as the total population's lifetime attributable risk (400),is 18.6. The “% Contribution” column represents the individual patient'stotal lifetime attributable risk (400) divided by the total population'slifetime attributable risk (400). The “running total” column representsthe sum of the individual patient's total lifetime attributable risk(400), plus all previous lifetime attributable risk (400) values. Thescaled lifetime attributable risk (410) column represents a roundedvalue for the “running total” multiplied by 100. Note that the lastrunning total value “1” is manually changed from 100 to 99 to representthe 99^(th) percentile and to accommodate a phantom patient with ahigher score that may one day appear in the population. This process ofcreating a scaled lifetime attributable risk (410) value utilizes thescore processor to transform the raw patient's total lifetimeattributable risk (400) values into a scaled value that provides muchmore insight into the relativity of lifetime attributable risk (400)values. One skilled in the art will appreciate that the generalpopulation data need not be analyzed each time a patient's scaledlifetime attributable risk (410) is determined, but may be summarized ina separate database or quick reference chart, diagram, or equation, justto a few embodiments that may simplify the process for large populationgroups.

In this example Patient L has a total lifetime attributable risk (400)value of 0.45%. The scaled lifetime attributable risk (410) for PatientL is equal to 19. A provider who has a basic understanding of theradiation and diagnostic study score (10) system will understand that ascaled lifetime attributable risk (410) of 19 implies that patients withthis level of risk (and lower) comprise about 19% of the total risk inthe population. A key threshold in the scaled lifetime attributable risk(410) can be found at a total lifetime attributable risk (400) of 1%.This happens with Patient B. A provider who understands that a scaledlifetime attributable risk (410) value of 50 represents a 1% totallifetime attributable risk (400) will also then understand that anyscaled lifetime attributable risk (410) value below 50 represents lessthan 1% total lifetime attributable risk (400). Furthermore, theadditional data point 4.53% total lifetime attributable risk(400)=scaled lifetime attributable risk (410) value of 99 informs aprovider on the general shape of the total lifetime attributable risk(400) curve in that it increases one unit in the first half of thescaled lifetime attributable risk (410) value range (0-49) and thenquadruples in the last half of the scaled lifetime attributable risk(410) value range (50-99), as seen in FIG. 21. In one embodiment theradiation and diagnostic study score (10) is simply the scaled lifetimeattributable risk (410) value, as graphically illustrated in the diagramof FIG. 11.

In a further embodiment a recent study indicator (500) is incorporatedin the process of creating the radiation and diagnostic study score(10). For example in one embodiment the transition from the scaledlifetime attributable risk (410) value to a radiation and diagnosticstudy score (10) is accomplished by the score processor adding a thirddigit to the scaled lifetime attributable risk (410) value, wherein thethird digit is a recent study indicator (500) which represents thenumber of recent radiologic tests (200) within a recent indicator timeperiod (510). The recent indicator time period (510) is one year orless, preferably six months or less, or more preferably 90 days or less.The number of radiologic tests (200) within the recent indicator timeperiod (510) can alert a provider to radiology data that may beclinically relevant even when a patient has a low radiation anddiagnostic study score (10). FIG. 7 represents the addition of a recentstudy indicator (500) to the scaled lifetime attributable risk (410)value to create the composite radiation and diagnostic study score (10)of this embodiment. As can be seen in FIG. 7, some patients with a lowcomposite radiation and diagnostic study score (10) have a high recentstudy indicator (500), or third digit count, indicating several recentradiologic tests (200) within the recent indicator time period (510).For instance, Patient X has a composite radiation and diagnostic studyscore (10) of 085 and even though a provider knows that patient X has arelatively low scaled lifetime attributable risk (410) (well less than1%), they also know from looking at the composite radiation anddiagnostic study score (10) that Patient X has had 5 radiologic tests(200) within the recent indicator time period (510) so perhaps theirclinical interest in Patient X's radiology history should be higher. Inthis embodiment the composite radiation and diagnostic study score (10)is normalized to 3 digits, by the score processor, with a leading zerowhen required, and it is also a composite 3 digit number wherein thefirst two digits represent the scaled lifetime attributable risk (410)value and the third digit represents the recent study indicator (500),or quantity of recent radiologic tests (200) within the recent indicatortime period (510), as graphically illustrated in the diagram of FIG. 12.

The radiation and diagnostic study score (10) described above is aunique and innovative approach to quantifying radiation risk anddiagnostic study usage and packaging it in a manner that is easy tostore, transport, and recognize. The radiation and diagnostic studyscore (10) can be used as a visual trigger raising the general awarenessof a treating provider but it can also be used as a discrete dataelement used within a computerized clinical decision supportapplication. For example, if a provider was going through the process toelectronically order a CT Scan for a patient, an alert could be providedin real-time if the patient has a radiation and diagnostic study score(10) above a predetermined score alert value (600), or if the recentstudy indicator (500) is above a predetermined recent study alert value(520), or if the same radiologic test (200) has been performed within aprior study period.

In another embodiment, the radiation and diagnostic study score (10) andother relevant information is transformed by the score processor into areport (700) format that may further explain the types of radiologicaltests (200) contributing to the score and when they were performed.Relative scoring information may also be included in the report (700) tohelp contextualize the numerical values and to aid patient education,should it be deemed necessary. An example of a report (700) is seen inFIGS. 19-21, which will be described later in more detail.

A living patient's record is not static and one can anticipate that apatient's radiology record will change, perhaps quite frequently. To bemost effective and useful, a radiation and diagnostic study score (10)(and report) may be computed in real-time at the point and time ofservice. This requires frequent updates of aggregated data, or the useof a real-time query system for distributed data.

While the above recent study indicator (500) embodiment having the lastdigit of the radiation and diagnostic study score (10) represents onemeasure of usage, a more sophisticated measurement of usage can be madeusing a similar method as the lifetime attributable risk (400) andscaled lifetime attributable risk (410), using the calculations andtransformations previously described. Whereas the lifetime attributablerisk (400) measurement is the product of the measure of ionizingradiation, mSv in this example, and the age adjustment factor (300),another usage metric embodiment utilizes the product of two othervariables, namely a previous study factor (800), also referred to as aPSF, and a time decay element (900), also referred to as a TDE.

The previous study factor (800) represents the number of times anindividual radiologic test (200) has been repeated for a patient. Thebelief in tracking this particular metric is that the more times anindividual radiologic test (200) is repeated the less the probableutility of each additional radiologic test (200) of the same type. Anumber of equations can be designed to generate the previous studyfactor (800). In one embodiment the previous study factor (800) may berepresented by the equation:previous study factor(800)=ln(repetitive test number(810))+1where a repetitive test number (810) represents the number of times theradiological test (200) has been performed. For this particularembodiment, FIG. 8 represents the previous study factor (800) value forthe first 20 times, repetitive test numbers (810) 1-20, a radiologictest (200) is ordered using the formula of this embodiment. In thisembodiment the previous study factor (800) increases in a non-linearmanner as a result of the example logarithmic function. Alternatively,in another embodiment the previous study factor (800) is a fixed valuegenerated by simply counting whether or not a study represents a repeat;thus in a further embodiment the previous study factor (800) is a 0 ora 1. One skilled in the art will appreciate that the score processor mayutilize any number of growth modeling techniques.

The time decay element (900) represents the amount of time that haspassed since a radiologic test (200) has been performed. The belief intracking this particular metric is that the more time that has elapsedsince a radiologic test (200) was performed, the less important thatindividual radiologic test (200) data is when considering whether toorder additional radiologic tests (200). A number of equations can bedesigned to generate the time decay element (900). In one embodiment thetime decay element (900) may be represented by the equation:time decay element(900)=0.999^(number of days elapsed)For this particular embodiment, FIG. 9 represents the time decay element(900) value for 10 days increments of elapsed time using this formula.One skilled in the art will appreciate that the score processor mayutilize any number of declining curve methodologies, including, but notlimited to, exponential, hyperbolic, and harmonic.

A usage metric (1000), which is the product of the previous study factor(800) and the time decay element (900), using the above exampleformulas, is depicted for Patient A in FIG. 10. One skilled in the artwill appreciate that this usage metric (1000) can be transformed by thescore processor into a scaled usage metric (1010) by considering arepresentative sample of the general population in the same manner aspreviously explained with respect to the total lifetime attributablerisk (400) being transformed into a scaled lifetime attributable risk(410), thereby resulting in a scaled usage metric (1010) ranging from0-99 as graphically illustrated in the diagram of FIG. 13.

This scaled usage metric (1010) can be used by the score processor tosort out patients along the lines of recent and repetitive testing. Itcan also be combined with the scaled lifetime attributable risk (410) ina weighted fashion to generate a composite score representing risk andusage. For instance in one embodiment if Patient A were determined tohave a scaled lifetime attributable risk (410) of 36 (as demonstrated inthe examples above) and a scaled usage metric (1010) of 54, an equallyweighted composite radiation and diagnostic study score (10) would beequal to 36/2+54/2=45. Further, in an additional embodimentincorporating a third digit representing the recent study indicator(500), or quantity of recent radiologic tests (200) within the recentindicator time period (510), if Patient A had 2 recent radiologic tests(200) within the recent indicator time period (510), a further compositeradiation and diagnostic study score (10) would be equal to 452, asgraphically illustrated in the diagram of FIG. 14.

In the previous embodiment the weighting of the scaled lifetimeattributable risk (410) and the scaled usage metric (1010) was equal,thus a scaled LAR weighting factor (420) was 0.5 and a scaled usageweighting factor (1020) was 0.5. One skilled in the art will realizethat the scaled LAR weighting factor (420) and the scaled usageweighting factor (1020) could be as low as zero, provided their sum addsup to 1.0. Thus, in the prior example explained with reference to FIGS.1-7, the composite radiation and diagnostic study score (10) can bethought of as being determined using a scaled usage weighting factor(1020) of zero and represents a pure scaled lifetime attributable risk(410) score. Conversely, a composite radiation and diagnostic studyscore (10) with a scaled LAR weighting factor (420) of zero wouldrepresent a pure scaled usage metric (1010) score. Independent of anycomposite scoring methodology the scaled lifetime attributable risk(410) and the scaled usage metric (1010) represented in this documentrepresent two individually useful metrics that may be used independentof one another to sort and report on large populations of patients. Inone further embodiment the scaled LAR weighting factor (420) ranges from0.2 to 0.8, the scaled usage weighting factor (1020) ranges from 0.2 to0.8, and the sum of the scaled LAR weighting factor (420) and the scaledusage weighting factor (1020) is 1.0, while in an even furtherembodiment the scaled LAR weighting factor (420) ranges from 0.4 to 0.6,the scaled usage weighting factor (1020) ranges from 0.4 to 0.6, and thesum of the scaled LAR weighting factor (420) and the scaled usageweighting factor (1020) is 1.0.

Yet another usage metric can be made using a similar method as thelifetime attributable risk (400) and scaled lifetime attributable risk(410), as well as the usage metric (1000) and the scaled usage metric(1010), using the calculations and transformations previously described.For instance, the general process of (1) assigning a variable to aradiologic study, (2) summing the values of that variable for individualpatients, and (3) creating a scaled metric by comparing the individual'ssum to a plurality of the population can be used with any metric ofvalue.

For example, the radiation and diagnostic scores (10) described aboveare heavily weighted towards radiological tests (200) that use ionizingradiation. However, many types of radiologic tests (200) involve noradiation whatsoever (i.e. MRI) but yield some of the highest qualityimages obtainable. Thus, knowledge of previous high-resolution imagesmay eliminate the need for further testing of the same region usinglower resolution approaches such as plain film x-ray. For this reason,in yet another embodiment a variable that captures the resolution valueof previous studies will add to the utility of the radiation anddiagnostic scores (10).

A resolution metric (1100) can be derived by assigning a resolutionvalue approximately equivalent to the amount of ionizing radiation usedin a radiologic test (200), with the general understanding that moreradiation generally results in high resolution or more complex images. Adifferent approach to assigning a resolution metric (1100) forradiologic tests (200) that don't use ionizing radiation (i.e. MRI andultrasound) is required. In the case of these two modalities, a MRI canbe thought of as being generally equivalent to a CT Scan, and anultrasound can be generally thought of as equivalent to plain filmx-rays. As such, in one embodiment a MRI can be assigned a resolutionmetric (1100) equal to the average of all CT Scans and an ultrasound canbe assigned a resolution metric (1100) equal to the average of all plainfilm x-rays.

Alternatively, in a further embodiment another method of resolutionassignment for individual radiologic tests (200) may utilize a thirdparty system that generally captures the radiologic complexity of thestudy involved. For example, Relative Value Units (RVUs) as dictated byMedicare are assigned to every CPT code and generally represent theexpertise required, associated practice expense, and liability expense.In practice, a radiologic reading of an MRI of an extremity may have anRVU assignment of approximately 2 and a plain film x-ray of the sameregion may have an RVU assignment of approximately 0.2.

FIG. 22 represents a total resolution metric (1100) calculation forPatient A. In this example, the radiation dose values, in mSv, from FIG.2 are used to as the resolution metric (1100) for radiologic tests (200)associated with ionizing radiation. However, as seen in FIG. 2, test 5did not have an associated radiation dose value, thus a value of zerowas used in FIG. 4. Now, we are going to account for this radiologictest (200) by assigning it a resolution metric (1100) equal to thehighest mSv value, namely a resolution metric (1100) of 20 as seen inFIG. 22. This example calculation is consistent with Test 4 beingrepresentative of a CT Scan and Test 5 representing an MRI. Thus, inthis embodiment the use of a resolution metric (1100) accounts forradiologic tests (200) associated with ionizing radiation, as well asthose that are not associated with ionizing radiation but should befactored in to the creation of the radiation and diagnostic study score(10).

A total resolution metric (1100) is depicted for Patient A in FIG. 22.One skilled in the art will appreciate that this resolution metric(1100) can be transformed by the score processor into a scaledresolution metric (1110) by considering a representative sample of thegeneral population in the same manner as previously explained withrespect to (a) the total lifetime attributable risk (400) beingtransformed into a scaled lifetime attributable risk (410), and (b) thepatient's total usage metric (1000) into a scaled usage metric (1010),thereby resulting in a scaled resolution metric (1110) ranging from 0-99as graphically illustrated in the diagram of FIG. 23. Alternatively, thetotal resolution metric (1100) calculation for Patient A shown in FIG.22, may assign a resolution metric (1100) of zero for radiologic tests(200) associated with ionizing radiation because some embodimentsincorporating a scaled lifetime attributable risk (410) have alreadyaccounted for these radiologic tests (200), and then, in this case, thetotal resolution metric (1100) and the scaled resolution metric (1110)represent the additional consideration associated with radiologic tests(200) that are not associated with ionizing radiation.

Regardless of the actual method used to assign a resolution metric(1100) value to the radiologic tests (200), as seen in FIGS. 23 and 24,one skilled in the art will appreciate that a scaled resolution metric(1110) can be generated and used in the same manner as the scaled usagemetric (1010) described above. This scaled resolution metric (1110) canthen be incorporated into the radiation and diagnostic scores (10)through the application of a scaled resolution weighting factor (1120).

This scaled resolution metric (1110) can also be combined with thescaled lifetime attributable risk (410) and/or the scaled usage metric(1010) in a weighted fashion to generate a composite score. For instancein one embodiment if Patient A were determined to have a scaled lifetimeattributable risk (410) of 36 (as demonstrated in the examples above), ascaled usage metric (1010) of 54, and a scaled resolution metric (1110)of 24, an equally weighted composite radiation and diagnostic studyscore (10) would be equal to 36/3+54/3+24/3=38. The weighted compositeradiation and diagnostic study score (10) may include any combination ofweighting 2 or more of the scaled resolution metric (1110), the scaledlifetime attributable risk (410), and/or the scaled usage metric (1010).Further, in an additional embodiment incorporating a third digitrepresenting the recent study indicator (500), or quantity of recentradiologic tests (200) within the recent indicator time period (510), ifPatient A had 2 recent radiologic tests (200) within the recentindicator time period (510), a further composite radiation anddiagnostic study score (10) would be equal to 382, as graphicallyillustrated in the diagram of FIG. 24.

In the previous embodiment the weighting of the scaled lifetimeattributable risk (410), the scaled usage metric (1010), and the scaledresolution metric (1110) were equal, thus a scaled LAR weighting factor(420) was ⅓, a scaled usage weighting factor (1020) was ⅓, and a scaledresolution weighting factor (1120) was ⅓. One skilled in the art willrealize that the scaled LAR weighting factor (420), the scaled usageweighting factor (1020), and the scaled resolution weighting factor(1120) can be as low as zero, provided their sum adds up to 1.0.Independent of any composite scoring methodology the scaled lifetimeattributable risk (410), the scaled usage metric (1010), and the scaledresolution metric (1110) represented in this document representindividually useful metrics that may be used independent of one another.For instance, having a scaled LAR weighting factor (420) of 1.0, with ascaled usage weighting factor (1020) of zero, and a scaled resolutionweighting factor (1120) of zero, effectively results in the radiationand diagnostic score (10) methodologies illustrated in FIGS. 11 and 12.Yet some health care providers may want to utilize a radiation anddiagnostic score (10) methodology that is more influenced by repeatedradiologic tests (200), in which case a scaled usage metric (1010) isfactored in by having a non-zero scaled usage weighting factor (1020),as illustrated in FIGS. 13 and 14. Even further, some health careproviders may want to utilize a radiation and diagnostic score (10)methodology that does not ignore radiologic tests (200) that are notassociated with ionizing radiation. In this situation the health careprovider may want to utilize a radiation and diagnostic score (10)methodology incorporating the resolution metric (1100) procedure alone,or weight it in combination with lifetime attributable risk (410) and/orthe scaled usage metric (1010), as illustrated in FIGS. 23 and 24. Thus,the many embodiments disclosed provide a health care provider toconsider many different factors in arriving at a radiation anddiagnostic score (10). Therefore it is anticipated that one health caresystem may prefer a radiation and diagnostic score (10) that onlyconsiders radiologic tests (200) associated with ionizing radiation;while a second health care system may a prefer radiation and diagnosticscore (10) that is influenced by repeated radiologic tests (200), aswould be the case with a methodology incorporating the usage metric(1000); while a third health care system may prefer a radiation anddiagnostic score (10) that is influenced by radiologic tests (200) thatare not associated with ionizing radiation.

In one embodiment the scaled LAR weighting factor (420) ranges from 0.1to 0.8, the scaled usage weighting factor (1020) ranges from 0.1 to 0.8,the scaled resolution weighting factor (1120) ranges from 0.1 to 0.8,and the sum of the three weighting factors is 1.0; while in anotherembodiment the scaled LAR weighting factor (420) ranges from 0.2 to 0.6,the scaled usage weighting factor (1020) ranges from 0.2 to 0.6, and thescaled resolution weighting factor (1120) ranges from 0.2 to 0.6, andthe sum of the three scaled weighting factors is 1.0; and in an evenfurther embodiment the scaled LAR weighting factor (420) ranges from 0.5to 0.9, while the scaled usage weighting factor (1020) and the scaledresolution weighting factor (1120) are each less than 0.5.

The above examples utilize a closed universe of 25 individuals (PersonsA-Y), all of variable age, who have been subject to only 5 possibleradiologic tests (Test 1-5) in an effort to simplify the explanation ofa complex invention, however one skilled in the art will appreciate thatthis invention is preferably applied to an open system in which peopleare added to, and removed from, the database (100) regularly. Similarlythe example was limited to 5 possible radiologic tests (200), a fractionof those present in the real world. Additional examples of radiologictest (200) data are seen in FIG. 16.

The previously mentioned database (100) may reside on a state medicalsystem server, however one skilled in the art will appreciate that thedatabase (100) described herein is not limited to a statewide system ora federal system, as it may be a hospital specific database, acommercial database, a data aggregator database, an insurance companydatabase, or community specific database (100). Similarly, the database(100) need not reside on a server but rather may reside on a localmemory device in a standalone manner, and further, in anticipation ofadvances in health care IT infrastructure, the database (100) may becreated for an individual patient by broadly electronically querying anetwork of health care providers and aggregating the collected data,which may be completed in virtually real-time. The radiologic test (200)data may be found in numerous locations. A hospital or imaging centermay store the information within its own Radiology Information System(RIS). A Health Information Exchange (HIE) may hold references to theRIS data, or the actual RIS data, for many regionally related hospitals.Billing information containing radiology data is transmitted to insurersfor payment and creates yet another source of radiology data, namely adatabase of Current Procedural Terminology (CPT) codes, Revenue Codes,and Health Care Common Procedure Coding System (HCPCS) Codes, aggregatedat the insurer level. Because patients often obtain diagnostic radiologyservices at a variety of locations, data aggregators such as an HIE orinsurance company represent some of the best sources of data, howeverone skilled in the art will appreciate that the database (100) describedherein is not limited to any of the above. Similarly, the database (100)need not reside on a server but rather may reside on a local memorydevice in a standalone manner, and further, in anticipation of advancesin health care IT infrastructure, the database (100) may be created foran individual patient by broadly electronically querying a network ofhealth care providers and aggregating the collected data, which may becompleted in virtually real-time. Regardless of the scope, location, orcreation of the database (100), it contains at least one of recordindicative of the imaging studies performed on a patient.

One illustration of patient data available in a database (100) is seenin FIG. 15. A record may contain patient data and radiologic test (200)data. Patient data may include information such as a unique patient IDand/or a patient birth date, in addition to any number of additionalpatient specific data that is not relevant to the present discussion.Radiologic test (200) data may include information such as imagingbilling code(s), imaging identifiers indicative of the type of imagingstudy performed and the area of the patient exposed to the study, theimaging date, and the imaging provider. In many cases, the record for apatient contains duplicate references to the same imaging event. Thismay occur as a result of a hospital submitting a bill capturing thetechnical performance of a CT Scan and a provider submitting a separatebill for the professional reading of the CT Scan. Addendums topreviously completed studies are another possible source of duplicity.Additionally, differences can exist in the date of service for the samestudy (where it is performed on one day and read on the next) and howindividual studies are classified. The end result of the duplicates anddifferences is that a collection of radiology records for a singlepatient often must be reduced to the set of studies that correspond tothe set of singular imaging events at the source of all records. Oneskilled in the art will appreciate that many methods of alternativedesign may be required to properly combine, normalize, and de-duplicatedata from disparate sources. In one particular embodiment the imagingdata consists solely of one or more billing codes and imaging dates,then the radiological exposure processor must then correlate the billingcodes to radiologic tests (200), which may include the use of a billingcode correlation protocol and/or database. For example, it is notuncommon for a single radiologic test (200) to result in three or fourbilling codes in the database (100). Thus, the billing code correlationprotocol and/or database recognizes common billing code combinations forcertain radiologic tests (200) and transforms the billing-code specificdata into a record that is radiologic test (200) or procedure specific.For instance to arrive at a radiologic test (200) of a “CT Head” theradiological exposure processor may need to recognize multiple imagingbilling codes commonly associated with a CT imaging study, and perhapsadditional imaging billing codes to ascertain that the CT imaging studywas associated with a specific area of the body, such as the head inthis example. Thus, the type of radiologic test (200) may be readilyavailable in the database (100) or it may need to be established by thebilling code correlation protocol and/or database.

As previously mentioned, the act of creating the scaled lifetimeattributable risk (410), and in some embodiments the scaled usage metric(1010) and/or a scaled resolution metric (1110), consists of comparing aparticular patient's total lifetime attributable risk (400), within thestudy period, with the total lifetime attributable risk associated witha large pool of patients that accurately represent the generalpopulation. In one embodiment the large pool of patients contains atleast 1000 patients, while another embodiment contains at least 500,000patients, and in an even further embodiment contains at least 1,000,000patients. In the big picture the comparison simply results in at leastan indication of where the patient data ranks when compared to similardata that is representative of a larger population of patients. Forexample, one embodiment may simply identify whether the patient data isin a below normal range, a normal range, or an above normal range whencompared to a larger population of patients. Alternatively, anotherembodiment may determine a percentile ranking of the patient datacompared to the larger population of patients.

As previously discussed, a scaled lifetime attributable risk (410) isderived by comparing the patient's total lifetime attributable risk(400) with the total lifetime attributable risk (400) for a plurality ofthe general population. In one embodiment this comparison is scaled to avalue that is proportional to the percentile ranking of the individualpatient risk within the general population. As such, the scaled lifetimeattributable risk (410) value for all patients will fall within a rangeof 0 to 99. The method of determining the scaled values is based oncalculating the total lifetime attributable risk (400) for a large groupof patents that accurately reflect the general population and rankordering them from lowest to highest. The % contribution to totalpopulation lifetime attributable risk is assigned to each patient andthe patient's individual contributions are then added until an integerpercentage is obtained. The total lifetime attributable risk values ateach integer transition point are used to establish a range for whichall total lifetime attributable risk values equate to a single scaledlifetime attributable risk (410) between 0-99. Calculating the totallifetime attributable risk for the large group of patients anddetermining integer transition points results in the graph seen in FIG.17.

In order to impart even more meaning into the radiation and diagnosticstudy score (10), in one embodiment the reference tables that form thefoundation of the comparative scoring paradigm were uniformlytransformed to align with recognizable risk data points inherent inmedicine. One simple, key risk metric in medicine is 1%. This metricaids many conversations and thoughts about risk as it represents a 1 in100 chance. For example, when discussing subarachnoid hemorrhage as asource of headache, the incidence in the emergency department populationof patients presenting with headache is thought to be approximately 1%.This 1% risk of subarachnoid hemorrhage has led to a strong emphasis ondetection, and has resulted in very sophisticated and detailed workupstrategies. Thus, in one embodiment, when creating a patient's scaledlifetime attributable risk (410), the population curve was shifted(intact) such that a scaled lifetime attributable risk (410) value of 50equates to 1% lifetime attributable risk (400). This shifted populationcurve, seen in FIG. 18 results in a scaled lifetime attributable risk(410) value of 99 being equal to 10% or greater LAR.

Regardless of exact embodiment previously disclosed, knowing that apatient has a radiation and diagnostic study score (10) of 805 informsthe provider of several key historical facts. On a scale of 000 to 999,an 805 is obviously skewed to the higher range and represents arelatively high radiation exposure level. In light of the unique way thepopulation incidence of risk was used to generate the scaled scores, theprovider knows that a person with a radiation and diagnostic study score(10) of 805 has a much higher (and rarer) exposure history than apatient with a radiation and diagnostic study score (10) of 305. Withknowledge and understanding of the relationship between total lifetimeattributable risk (400) and the radiation and diagnostic study score(10) system, the provider can also deduce that the patient with aradiation and diagnostic study score (10) of 805 has a lifetimeattributable risk of approximately 3%. Further, clearly this example isan embodiment having a recent study indicator (500), thus the 5 in theradiation and diagnostic study score (10) of 805 indicates there areseveral recent radiologic tests (200) the provider should be aware of.

As a result of the methodology used in creating the radiation anddiagnostic study score (10) system, providers will now have access to anintuitive scoring system that matches with their clinical expectationand matches with the prevalence of radiology usage and radiationexposure within the general population. Providers will be able to easilycompare patient scores and associated risk and more easily explain topatients what their risk score means in real terms. In some embodimentsa provider will be able to explain to a patient that a score of 500means they have acquired an approximate 1% additional risk of cancer,and if their score continues to increase, they will approach a 10% orgreater additional risk as they get closer to a radiation and diagnosticstudy score (10) of 990, as seen in FIG. 21. Thus, one embodimentfurther includes the step of scaling the general population radiologictest data so that the total lifetime attributable risk (400) ofapproximately unity is associated with a predetermined scaled lifetimeattributable risk (410). In an even further embodiment the predeterminedscaled lifetime attributable risk (410) associated with the totallifetime attributable risk (400) of approximately unity is approximately50. Similarly for the total usage metric, one embodiment furtherincludes the step of scaling the general population radiologic test dataso that the total usage metric (1000) of approximately unity isassociated with a predetermined scaled usage metric (1010). In an evenfurther embodiment the predetermined scaled usage metric (1010)associated with the total usage metric (1000) of approximately unity isapproximately 50.

Initial studies on a large population of almost 900,000 patients revealthat approximately 1-2% of the population has a radiation and diagnosticstudy score (10) of 500 or greater. This 1-2% of the population alsoaccounts for about 25% of the CT Scans and associated radiation exposurein the entire population, so although one would expect that a score of500 would be rarely seen, this initial study predicts that about 1 in 4CT scans will be performed on a patient with a score of 500 or greater.

Throughout this document there are multiple references to a step ofcomparing a quantity with the plurality of general population data todetermine a scaled value, whether it be the scaled lifetime attributablerisk (410), the scaled usage metric (1010), or the scaled resolutionmetric (1110). In some of the many disclosed embodiments thedetermination of an alert value includes a determination of whether thequantity is within an acceptable range or an unacceptable range, howeverother embodiments determine approximate percentile rankings of thequantity compared to the general population data. In one embodiment thegeneral population radiological exposure data referenced is dataassociated with at least 1000 patients over the period of interest. Inone embodiment this general population data is present in the database(100) and is extracted by the score processor for use in arriving at thescaled values. The general population data need not be extracted by thescore processor each time patient specific data is retrieved from thedatabase (100); rather the general population data may be extractedafter extended intervals, which may be months or even years. Therefore,the act of comparing a quantity with the plurality of general populationdata to determine a scaled value may include the step of previouslyacquiring the general population data, processing the data, convertingthe data into a quickly accessible electronic format, and storing theconverted data on hardware for use in determining the radiation anddiagnostic study score (10) in less than 5 seconds, whether the generalpopulation is local or on a hardware device on the other side of theplanet. Thus, in one embodiment a local score processor securelyretrieves and stores into memory patient specific data from a database(100), the local score processor securely retrieves and stores intomemory previously compiled and transformed data representative of thegeneral population radiological exposure, the local score processorretrieves portions of this stored data to form and store at least ascaled lifetime attributable risk (410) value, and transform the datainto the radiation and diagnostic study score (10), and the local scoreprocessor formats and transmits the radiation and diagnostic study score(10) to display on a visual media. Further, in light of confidentialpatient data security, the local score processor may then clear thepatient specific data from the local memory, as well as leave atimestamp within the remote database (100) to serve as an indicator ofwhen a patient's data was accessed. The score processor may furthersecurely transmit the radiation and diagnostic study score (10) back tothe database (100) for storage and retrieval during subsequent datarequests. Thus, a system for carrying out the determination of aradiation and diagnostic study score (10) may consist of severalsecurely connected pieces of hardware communicating with the speciallyprogrammed score processor to determine the radiation and diagnosticstudy score (10). As the local score processor retrieves the patientspecific data from the database (100), it may create a localpatient-specific database for temporarily storing and processing data.The local patient-specific database may be cleared of patient specificdata upon the creation of the radiation and diagnostic study score (10)and any associated reports that are simultaneously created.

The score processor is a specially programmed computer device such as apersonal computer, a portable phone, a multimedia reproduction terminal,a tablet, a PDA (Personal Digital Assistant), or a dedicated portableterminal that can perform the secure retrieval and processing of input,output, storage and the like of information. It goes without saying thatsuch a program can be distributed through a recording medium such as aCD-ROM and a transmission medium such as the Internet. Further, thepresent invention may be a computer-readable recording medium such as aflexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, aDVD-RAM, a BD (Blu-ray Disc), flash drives, thumb drives, and asemiconductor memory that records the computer program. Thus, thedistributed program may be used to program a computer to create a scoreprocessor thereby becoming a special purpose computer to securelyperform particular functions pursuant to instructions from programsoftware.

The radiation and diagnostic study score (10) and/or report (700) may bedisplayed on visual media. Such visual media referenced herein may be aCathode Ray Tube (CRT) monitor, a Liquid Crystal Display (LCD) monitor,a plasma monitor, a PC, a laptop, a tablet, a smart phone, a projectorand screen, paper, and/or any other such visual display device known tothose of ordinary skill in the art.

The development of a mechanism to generate an intuitive and meaningfullyrich radiation and diagnostic study score (10) significantly advancesthe state of art. It may be preferable to also have a radiation anddiagnostic study score (10) distribution and report mechanism to reachmaximum effect. The report (700) is the vehicle for communicating thedetails behind the radiation and diagnostic study score (10). The reportalso guides the provider in understanding the radiation basics behindthe assessment and enables individually relevant discussions with thepatient. FIG. 19 illustrates one embodiment of a front page of thereport (700), and FIGS. 20 and 21 illustrates one embodiment of a rearpage of the report (700), which demonstrates how the steps required togenerate the score can be broken down and displayed in a clinicallymeaningful manner.

One particular embodiment of FIG. 19 shows a report (700) in which thescore processor has transformed the raw data into a graphic “studytimeline” section in which the frequency and relative amount of ionizingradiation associated with a patient's radiologic tests (200) can beeasily visualized. Each vertical bar represents a radiologic test (200)and the height of the vertical bar is an indication of the amount ofionizing radiation associated with the particular radiologic test. In afurther embodiment, imaging studies not associated with ionizingradiation such as MRI's and ultrasounds are also displayed in thegraphical “study timeline,” but are separate from the ionizing radiationradiologic tests (200), in this case below the x-axis timeline, tofurther aid a provider in identifying duplicative imaging studies;however, as previously disclosed such non-ionizing radiation radiologictests (200) may be incorporated in the radiation and diagnostic studyscore (10) via associating them with a resolution metric (1100).

The distribution mechanism for the radiation and diagnostic study score(10) and report (700) can be as hard copy, an electronic image file suchas a PDF, a web service that accepts patient IDs and returns a radiationand diagnostic study score (10) as data and reports (700) as an imagefile, and additionally can adhere to an HL7 message standard and providethe scores and report as results. The report (700) can also be deliveredas an interactive web page that allows the user to drill down and obtainfiner detail to include impression and finding data. One skilled in theart will recognize that a multitude of distribution mechanisms exist forthe scores and reports, and the above represent only an exemplary subsetof the embodiments.

Numerous alterations, modifications, and variations of the preferredembodiments disclosed herein will be apparent to those skilled in theart and they are all anticipated and contemplated to be within thespirit and scope of this application. For example, although specificembodiments have been described in detail, those with skill in the artwill understand that the preceding embodiments and variations can bemodified to incorporate various types of substitute and or additional oralternative steps, procedures, and the order for such steps andprocedures. Accordingly, even though only few variations of the presentmethodology and system are described herein, it is to be understood thatthe practice of such additional modifications and variations and theequivalents thereof, are within the spirit and scope of thisapplication. The corresponding structures, materials, acts, andequivalents of all methods, means, and steps plus function elements inthe claims below are intended to include any structure, material, oracts for performing the functions in combination with other claimedelements as specifically claimed.

We claim:
 1. A method for determining a patient radiation and diagnosticstudy score (10) associated with a series of past diagnostic radiologictests associated with a patient, and treating the patient to manage therisk of cancer associated with future radiologic testing, comprising thesteps of: a) accessing, by at least one score processor, a database(100) and retrieving data indicative of a plurality of a patient's pastradiologic tests (200) during a scoring period, wherein the plurality ofpast radiologic tests (200) includes at least two different tests; b)associating, by the at least one score processor, a measure of ionizingradiation with each of the plurality of the patient's past radiologictests (200) from the scoring period; c) forming, by the at least onescore processor, a lifetime attributable risk (400) for each of theplurality of the patient's past radiologic tests (200) from the scoringperiod, and creating a total lifetime attributable risk (400) for thescoring period; d) transforming, by the at least one score processor,the total lifetime attributable risk (400) for the scoring period into ascaled lifetime attributable risk (410) for the scoring period bycomparing the total lifetime attributable risk (400) for the scoringperiod with general population radiologic test data, wherein the scaledlifetime attributable risk (410) indicates a measure of the totallifetime attributable risk (400) relative to the general population,further including the step of scaling the general population radiologictest data so that the total lifetime attributable risk (400) ofapproximately unity is associated with a predetermined scaled lifetimeattributable risk (410); e) creating, by the at least one scoreprocessor, the patient radiation and diagnostic study score (10) fromthe scaled lifetime attributable risk (410) to display on a visualmedia; and f) treating the patient and directing future radiologictesting at least in part on the patient radiation and diagnostic studyscore (10).
 2. The method of claim 1, wherein the step of forming thelifetime attributable risk (400) for each of the patient's pastradiologic tests (200) within the scoring period is further modified byan age adjustment factor (300), wherein the at least one score processordetermines the age adjustment factor (300) by analyzing a patient birthdate retrieved from the database (100) and a radiologic test date foreach radiologic test (200) retrieved from the database (100), andwherein the at least one score processor scales the measure of ionizingradiation for each radiologic test (200) by the age adjustment factor(300) for each radiologic test (200) to form the lifetime attributablerisk (400) for each radiologic test (200).
 3. The method of claim 1,wherein the predetermined scaled lifetime attributable risk (410)associated with the total lifetime attributable risk (400) ofapproximately unity is approximately
 50. 4. The method of claim 1,wherein the step of creating, by the at least one score processor, thepatient radiation and diagnostic study score (10) from the scaledlifetime attributable risk (410) to display on a visual media, furtherincludes the step of determining a recent study indicator (500)indicative of the number of radiologic tests (200) performed within arecent indicator time period (510) of no more than one year andincluding the recent study indicator (500) as a single digit in thepatient radiation and diagnostic study score (10).
 5. The method ofclaim 4, further including the steps of creating, by the at least onescore processor, an indicator alert if the patient has the recent studyindicator (500) is above a predetermined recent study alert value (520),and treating the patient and directing future radiologic testing atleast in part on the indicator alert.
 6. The method of claim 1, furtherincluding the steps of creating, by the at least one score processor, ascore alert if the patient has the radiation and diagnostic study score(10) is above a predetermined score alert value (600), and treating thepatient and directing future radiologic testing at least in part on thescore alert.
 7. A method for determining a patient radiation anddiagnostic study score (10) associated with a series of past diagnosticradiologic tests associated with a patient, comprising the steps of: a)accessing, by at least one score processor, a database (100) andretrieving data indicative of a plurality of a patient's past radiologictests (200) during a scoring period, wherein the plurality of pastradiologic tests (200) includes at least two different tests; b)associating, by the at least one score processor, a previous studyfactor (800) for each radiologic test (200) from the scoring period thathas been performed 2 or more times; c) associating, by the at least onescore processor, a time decay element (900) for each radiologic test(200) from the scoring period that has been performed 2 or more times;d) forming, by the at least one score processor, a test specific usagemetric (1000) for each of the patient's past radiologic tests (200) fromthe scoring period wherein the at least one score processor scales theprevious study factor (800) for each radiologic test (200) by the timedecay element (900) for each radiologic test (200) to form the testspecific usage metric (1000) for each radiologic test (200), and usingthe test specific usage metric (1000) for each radiologic test (200) tocreate a total usage metric (1000) for the scoring period; e)transforming, by the at least one score processor, the total usagemetric (1000) for the scoring period into a scaled usage metric (1010)for the scoring period by comparing the total usage metric (1000) forthe scoring period with general population radiologic test data, whereinthe scaled usage metric (1010) indicates a measure of the total usagemetric (1000) relative to the general population; f) creating, by the atleast one score processor, the patient radiation and diagnostic studyscore (10) from the scaled usage metric (1010) to display on a visualmedia; and g) treating the patient and directing future radiologictesting at least in part on the patient radiation and diagnostic studyscore (10) to manage the risk of cancer associated with radiologictests.
 8. The method of claim 7, wherein the step of creating, by the atleast one score processor, the patient radiation and diagnostic studyscore (10) from the scaled usage metric (1010) to display on the visualmedia, further includes the step of determining a recent study indicator(500) indicative of the number of radiologic tests (200) performedwithin a recent indicator time period (510) of less than one year andincluding the recent study indicator (500) as a single digit in thepatient radiation and diagnostic study score (10).
 9. A method fordetermining a patient radiation and diagnostic study score (10)associated with a series of past diagnostic radiologic tests associatedwith a patient, comprising the steps of: a) accessing, by at least onescore processor, a database (100) and retrieving data indicative of apatient's past radiologic tests (200) during a scoring period; b)associating, by the at least one score processor, a measure of ionizingradiation with each of the patient's past radiologic tests (200) fromthe scoring period; c) associating, by the at least one score processor,a previous study factor (800) for each radiologic test (200) from thescoring period that has been performed 2 or more times; d) associating,by the at least one score processor, a time decay element (900) for eachradiologic test (200) from the scoring period that has been performed 2or more times; e) forming, by the at least one score processor, alifetime attributable risk (400) for each of the patient's pastradiologic tests (200) from the scoring period, and creating a totallifetime attributable risk (400) for the scoring period; f) forming, bythe at least one score processor, a test specific usage metric (1000)for each of the patient's past radiologic tests (200) from the scoringperiod wherein the at least one score processor scales the previousstudy factor (800) for each radiologic test (200) by the time decayelement (900) for each radiologic test (200) to form the test specificusage metric (1000) for each radiologic test (200), and using the testspecific usage metric (1000) for each radiologic test (200) to create atotal usage metric (1000) for the scoring period; g) transforming, bythe at least one score processor, the total lifetime attributable risk(400) for the scoring period into a scaled lifetime attributable risk(410) for the scoring period by comparing the total lifetimeattributable risk (400) for the scoring period with general populationradiologic test data, wherein the scaled lifetime attributable risk(410) indicates a measure of the total lifetime attributable risk (400)relative to the general population; h) transforming, by the at least onescore processor, the total usage metric (1000) for the scoring periodinto a scaled usage metric (1010) for the scoring period by comparingthe total usage metric (1000) for the scoring period with generalpopulation radiologic test data, wherein the scaled usage metric (1010)indicates a measure of the total usage metric (1000) relative to thegeneral population; i) creating, by the at least one score processor,the patient radiation and diagnostic study score (10) from the (i) theproduct of the scaled lifetime attributable risk (410) and a scaled LARweighting factor (420), and (ii) the product of the scaled usage metric(1010) and a scaled usage weighting factor (1020), and displaying thepatient radiation and diagnostic study score (10) on a visual media; andj) treating the patient and directing future radiologic testing at leastin part on the patient radiation and diagnostic study score (10) tomanage the risk of cancer associated with radiologic tests.
 10. Themethod of claim 9, wherein the scaled LAR weighting factor (420) rangesfrom 0.0 to 1.0, the scaled usage weighting factor (1020) ranges from0.0 to 1.0, and the sum of the scaled LAR weighting factor (420) and thescaled usage weighting factor (1020) is 1.0.
 11. The method of claim 10,wherein the scaled LAR weighting factor (420) ranges from 0.2 to 0.8,the scaled usage weighting factor (1020) ranges from 0.2 to 0.8, and thesum of the scaled LAR weighting factor (420) and the scaled usageweighting factor (1020) is 1.0.
 12. The method of claim 9, wherein thestep of forming the lifetime attributable risk (400) for each of thepatient's past radiologic tests (200) within the scoring period isfurther modified by an age adjustment factor (300), wherein the at leastone score processor determines the age adjustment factor (300) byanalyzing a patient birth date retrieved from the database (100) and aradiologic test date for each radiologic test (200) retrieved from thedatabase (100), and wherein the at least one score processor scales themeasure of ionizing radiation for each radiologic test (200) by the ageadjustment factor (300) for each radiologic test (200) to form thelifetime attributable risk (400) for each radiologic test (200).
 13. Themethod of claim 9, further including the steps of: a) associating, bythe at least one score processor, a resolution metric (1100) with atleast each of the patient's past radiologic tests (200) that are notassociated with ionizing radiation, from the scoring period, and usingeach test specific resolution metric (1100) to create a total resolutionmetric (1100) for the scoring period; b) transforming, by the at leastone score processor, the total resolution metric (1100) for the scoringperiod into a scaled resolution metric (1110) for the scoring period bycomparing the total resolution metric (1100) for the scoring period withgeneral population radiologic test data, wherein the scaled resolutionmetric (1110) indicates a measure of the total resolution metric (1100)relative to the general population; and c) creating, by the at least onescore processor, the patient radiation and diagnostic study score (10)from (i) the product of the scaled lifetime attributable risk (410) andthe scaled LAR weighting factor (420), (ii) the product of the scaledusage metric (1010) and the scaled usage weighting factor (1020), and(iii) the product of the scaled resolution metric (1110) and a scaledresolution weighting factor (1120), and displaying the patient radiationand diagnostic study score (10) on the visual media.
 14. The method ofclaim 13, wherein the scaled LAR weighting factor (420) ranges from 0.0to 1.0, the scaled usage weighting factor (1020) ranges from 0.0 to 1.0,the scale resolution weighting factor ranges from 0.0 to 1.0, and thesum of the scaled LAR weighting factor (420), the scaled usage weightingfactor (1020), and scaled resolution weighting factor (1120) is 1.0. 15.The method of claim 14, wherein the scaled LAR weighting factor (420)ranges from 0.5 to 0.9, the scaled usage weighting factor (1020) is lessthan 0.5, the scaled resolution weighting factor (1120) is less than0.5, and the sum of the scaled LAR weighting factor (420), the scaledusage weighting factor (1020), and scaled resolution weighting factor(1120) is 1.0.